| 基于SVM概率输出与证据理论的多分类方法 |
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| 引用本文： | 权文,王晓丹,王坚,张玉玺.基于SVM概率输出与证据理论的多分类方法[J].计算机工程,2012,38(5):167-169. |
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| 作者姓名： | 权文 王晓丹 王坚 张玉玺 |
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| 作者单位： | 1. 空军工程大学导弹学院,陕西三原,713800
2. 空军工程大学导弹学院,陕西三原713800;西北工业大学计算机学院,西安710072 |
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| 摘 要： | 单一技术无法有效解决多类分类问题。为此，提出一种基于一对多支持向量机(SVM)的基本概率分配输出方法，并与置信最大熵模型的D-S证据组合方法结合，给出基于SVM概率输出和证据理论的多分类模型。在3种UCI标准数据集上的仿真结果表明，该方法的分类精度优于传统的一对多和一对一硬输出方法，是一种有效的多类分类方法。
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| 关 键 词： | 证据理论 支持向量机 输出概率建模 信息融合 |
| 收稿时间： | 2011-11-11 |
Multi-class Classification Method Based on SVM Probability Output and Evidence Theory |
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| QUAN Wen
,
WANG Xiao-dan
,
WANG Jian
,
ZHANG Yu-xi.Multi-class Classification Method Based on SVM Probability Output and Evidence Theory[J].Computer Engineering,2012,38(5):167-169. |
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| Authors: | QUAN Wen WANG Xiao-dan WANG Jian ZHANG Yu-xi |
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| Affiliation: | 1(1.Missile Institute,Air Force Engineering University,Sanyuan 713800,China;2.College of Computer,Northwestern Polytechnical University,Xi’an 710072,China) |
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| Abstract: | One-technology do not solve multi-class classification problem,on the basis of this,a basic probability output distribution method based on One-Against-All(OAA) Support Vector Machine(SVM) is proposed,a multi-class model based on Support Vector Machine(SVM) probability output and evidence theory is put forward by integrating one-against-all multi-class SVM with max-entropy D-S theory,.Simulations results on three datasets of UCI repository show that the method has higher classification precision than hard output method OAA and OAO. |
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| Keywords: | evidence theory Support Vector Machine(SVM) output probability modeling information fusion |
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